DIGITAL BIOMARKERS AND ARTIFICIAL INTELLIGENCE: THE FUTURE OF PRECISION ONCOLOGY AND PERSONALIZED THERAPEUTICS

Abstract

Digital biomarkers have emerged as a transformative component of precision oncology by enabling continuous, objective, and multidimensional assessment of cancer biology through integration of artificial intelligence (AI), multimodal biomedical data, wearable technologies, imaging, and molecular profiling. Unlike conventional biomarkers that are often measured at isolated time points, digital biomarkers provide dynamic longitudinal information capable of capturing disease evolution, therapeutic response, treatment-related toxicity, and patient physiology throughout the cancer care continuum. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, patient-reported outcomes, and real-world clinical evidence into unified computational frameworks. These intelligent systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen digital biomarker ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of digital biomarkers and artificial intelligence, emphasizing their transformative role in precision oncology and personalized therapeutics.